Loyalty of on-Line Bank Customers

1999 ◽  
Vol 14 (4) ◽  
pp. 375-386 ◽  
Author(s):  
Leif B. Methlie ◽  
Herbjørn Nysveen
Keyword(s):  
Customer Value ◽  
Explanatory Power ◽  
Search Costs ◽  
Marketing Strategies ◽  
Market Place ◽  
Traditional Markets ◽  
Brand Reputation ◽  
Value Propositions ◽  
On Line ◽  
Business Sectors

Electronic commerce changes the relationships between sellers and buyers dramatically. The new properties of electronic markets offer customers added values. New customer value propositions have to be established in most markets and new marketing strategies must be formulated. One of the business sectors most heavily affected is banking. How can banks retain loyal customers when moving into electronic banking? Research in marketing has unveiled several determinants of customer loyalty. However, this knowledge is based on research on customers in traditional markets. In this paper we focus on customers’ loyalty in on-line banking. The findings indicate that determinants of loyalty in on-line banking environments are similar to those in the physical market-place. However, customer satisfaction is found to have the most significant impact, followed by brand reputation, while switching costs and search costs, although significant, have minor explanatory power. The implications of these findings on banks’ marketing strategies are discussed.

SRI’s turnaround: how four principles guided high-value, market-creating innovation

2017 ◽  
Vol 45 (4) ◽  
pp. 3-8
Author(s):  
Steve Denning
Keyword(s):  
Business Model ◽  
Customer Value ◽  
Large Scale ◽  
Leadership Strategies ◽  
Content Type ◽  
Value Propositions ◽  
On Line ◽  
Market Needs ◽  
Working Hypotheses ◽  
Practical Implications

Purpose The article explores the leadership strategies of a CEO who defied the odds against culture change and accomplished a storied turnaround, Curt Carlson who introduced a culture of innovation at SRI International. Design/methodology/approach Under Carlson’s leadership, SRI developed a methodology for rapid, large-scale, serial innovation, starting with a focus on important customer and market needs. The innovation proposals had to develop compelling hypotheses for both the product offering and the business model. Findings Need, Approach, Benefits per costs and Competition (NABC). the methodology Carlson and his team developed, contains the fundamental framework for creating customer value, it applies to the entire enterprise. It brings all functions together using a short, easy to remember meme that starts every conversation with a focus on customer need. Practical implications One of the most spectacular and best-known SRI innovation wins was Siri, the intelligent personal computer assistant and on-line knowledge navigator, an integral part of the iPhone. As a case example, Siri illustrates the power of the NABC approach. Originality/value Carlson stresses that the key element in SRI’s success with Siri was not just the technology. It was getting the entire value proposition right. “One of the things that changed at SRI was the realization that we needed to have really solid working hypotheses, both for the product and the business model, before we started spending significant money on technology. That’s one of the biggest mistakes firms make. They rush ahead and want to build the product before they de-risk their value propositions.

scholarly journals Who Donates on Line? Segmentation Analysis and Marketing Strategies Based on Machine Learning for Online Charitable Donations in Taiwan

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 52728-52740
Author(s):  
Chiung-Wen Hsu ◽  
Yu-Lin Chang ◽  
Tzer-Shyong Chen ◽  
Te-Yi Chang ◽  
Yu-Da Lin
Keyword(s):  
Machine Learning ◽  
Marketing Strategies ◽  
Charitable Donations ◽  
Segmentation Analysis ◽  
On Line ◽  
Line Segmentation

Improving Customer Value Index and Consumption Forecasts using a Weighted RFM Model and Machine Learning Algorithms

2022 ◽  
Vol 30 (3) ◽  
pp. 0-0
Keyword(s):  
Customer Value ◽  
Marketing Strategies ◽  
Machine Learning Algorithms ◽  
Entropy Weight ◽  
Rfm Model ◽  
Consumption Data ◽  
Entropy Weight Method ◽  
Customer Profiling ◽  
Weight Method ◽  
Rmf Model

Collecting and mining customer consumption data are crucial to assess customer value and predict customer consumption behaviors. This paper proposes a new procedure, based on an improved Random Forest Model by: adding a new indicator, joining the RFMS-based method to a K-means algorithm with the Entropy Weight Method applied in computing the customer value index, classifying customers to different categories, and then constructing a consumption forecasting model whose RMSE is the smallest in all kinds of data mining models. The results show that identifying customers by this improved RMF model and customer value index facilitates customer profiling, and forecasting customer consumption enables the development of more precise marketing strategies.

Gender differences in Taiwan’s hypermarkets

2016 ◽  
Vol 28 (4) ◽  
pp. 650-662 ◽  
Author(s):  
Tsung-Sheng Chang ◽  
Hsiaoping Yeh
Keyword(s):  
Gender Differences ◽  
Customer Behavior ◽  
Marketing Strategies ◽  
Male And Female ◽  
Content Type ◽  
Product Categories ◽  
Traditional Markets ◽  
Key Factor ◽  
Practical Implications

Purpose Hypermarkets face a very competitive environment, as their rivals also include traditional markets, convenient stores and supermarkets. In addition to offering innovative services, hypermarket managers must also pay attention to customer shopping patterns and behaviors in order to gain market share. The purpose of this paper is to posit that there will be significant differences between male and female hypermarket customers in terms of both shopping time and categories of goods purchased. Design/methodology/approach To investigate actual shopping behaviors, this study observed customers in hypermarkets and recorded how long they spent shopping and what they purchased. Findings The results of this study implied that gender is a key factor that affects both shopping time and the goods purchased in Taiwanese hypermarkets. The empirical findings also indicated that customers favor certain goods categories. Practical implications The results of this study provide details of the differences between male and female hypermarket customers, with which managers can use to develop better marketing strategies. Originality/value The purpose of this study is to show hypermarket retailers how gender differences affect customer behavior in this context. It also presents a consumer product categories list for hypermarkets, and this can be used as a reference for follow-up studies.

Data Mining Applications in the Hospitality Industry

2010 ◽  
pp. 1109-1114
Author(s):  
Soo Kim
Keyword(s):  
Data Mining ◽  
Customer Value ◽  
Hospitality Industry ◽  
Marketing Strategies ◽  
Restaurant Industry ◽  
Data Mining Techniques ◽  
Competitive Edge ◽  
Processing Data ◽  
Hospitality Managers

Some people say that “success or failure often depends not only on how well you are able to collect data but also on how well you are able to convert them into knowledge that will help you better manage your business (Wilson, 2001, p. 26).” It is said the $391 billion restaurant industry generates a massive amount of data at each purchase (Wilson, 2001), and once collected, such collected data could be a gigantic tool for profits. In the hospitality industry, knowing your guests in terms of where they are from, how much they spend money, and when and what they spend it can help hospitality managers formulate marketing strategies, enhance guest experiences, increase retention and loyalty and ultimately, maximize profits. Data mining techniques are suitable for profiling hotel and restaurant customers due to their proven ability to create customer value (Magnini, Honeycutt, & Hodge, 2003; Min, Min & Emam, 2002). Furthermore, if the hospitality industry uses such data mining processes as collecting, storing, and processing data, the industry can get strategic competitive edge (Griffin, 1998). Unfortunately, however, the hospitality industry and managers are behind of using such data mining strategies, compared to the retail and grocery industries (Bogardus, 2001; Dev & Olsen, 2000). Therefore, there is a need for learning about such data mining systems for the hospitality industry. The purpose of this paper is to show the applications of data mining systems, to present some successes of the systems, and, in turn, to discuss some benefits from the systems in the hospitality industry.

Role of Consumer Knowledge in Developing Purchase Intentions and Driving Services Efficiency across Marketing Channels in Mexico

2016 ◽  
pp. 204-226
Author(s):  
Rajagopal
Keyword(s):  
Purchase Intentions ◽  
Marketing Channels ◽  
Marketing Strategies ◽  
Primary Data ◽  
Level Of Education ◽  
Or Groups ◽  
Value Propositions ◽  
High Level

This study is carried out in Mexico with an objective to analyse empirically the role of education in a transforming services marketing strategies of the firms. The study is carried on in Mexico through pragmatic investigation among the consumers subscribing to the communication and entertainment services. The analysis of primary data is developed around the theory of action that demonstrates the skills and confidence of individuals or groups towards making decision in acquiring or hiring services to improve their quality of life. The results of the study reveal that knowledge acquired on the services and value perceived by the consumers play key role in determining the intentions to purchases services. This study meticulously rows several arguments on how consumers with high level of education scrutinize the benefits offered by the firms marketing their communication and entertainment services, and build their value propositions on the services bought or contracted.

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